Recently, there has been increased interest in IoT technology capable of information sharing between devices, sensors, and machines through connection to the Internet as the future technology. IoT technology enables new services and applications to provide environment monitoring, automatic measurement, and smart sensing functions through devices with connectivity added thereto, thereby contributing to the improvement of human life.
To this end, wireless systems must be able to support a variety of needs such as high transmission rates, low latency, and enhanced reliability and energy efficiency. That is, one consideration in designing an IoT system is that IoT devices must be designed with significant hardware limitations and power budgets. For example, IoT devices need to use a narrowband RF chain, a small number of antennas, a low-capacity memory, and a low-power signal processing unit. Also, since systems must often switch to a sleep mode in order to save energy, it is not possible to continuously measure time and frequency channels. Furthermore, since pilot signals are uniformly allocated in the frequency domain on current wireless systems, it is not possible to feed back CSI of the entire system band using samples acquired from narrow-band measurements. In this situation, the systems cannot obtain the benefit of frequency selection scheduling for the entire band using partial CSI, and thus cannot support various types of devices.
In order to solve this problem, that is, in order to reduce pilot overhead and improve channel estimation performance, Compressed Sensing (CS) based on pilot transmission and channel estimation techniques has been studied in recent years. Through this technology, recovery performance may be enhanced by using a common sparse signal structure for time-domain channel vectors. Although a CS-based time-domain channel estimation algorithm is effective in reducing pilot overhead, the pilot overhead increases linearly with the number of antennas. In fact, the performance of the CS algorithm depends solely on channel vector sparsity for each antenna, and thus pilot overhead and computational complexity increase in proportion to the number of antennas. Therefore, the CS algorithm is not suitable for IoT environments.